Per-cell Bayesian optimization of handover parameters beats fixed 3GPP settings in a simulated urban network, while reinforcement learning matches it with transfer learning.
Capacity an d power con- sumption of multi-layer 6G networks using the upper mid-ban d,
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Data-Driven Cellular Mobility Management via Bayesian Optimization and Reinforcement Learning
Per-cell Bayesian optimization of handover parameters beats fixed 3GPP settings in a simulated urban network, while reinforcement learning matches it with transfer learning.